Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning
Background: There is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs. Objective: We aimed to...
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| description | Background: There is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs.
Objective: We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs.
Methods: We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multilabel classifications using the deep neural language model Bidirectional Encoder Representations from Transformers (BERT) "DeepPavlov," which was pretrained on a Russian language corpus and applied to the Telegram messages. The resulting area under the curve score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea.
Results: Telegram users complained mostly about pain (5461/11,515, 47.43%), fever (5363/11,515, 46.57%), fatigue (3862/11,515, 33.54%), and headache (2855/11,515, 24.79%). Women reported more AEs than men (1.2-fold, P |
| doi_str_mv | 10.2196/30529 |
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| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_crossref_primary_10_2196_30529</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_c2d81755951f4b1dadcc123aeb24d46d</doaj_id><sourcerecordid>2583448835</sourcerecordid><originalsourceid>FETCH-LOGICAL-c457t-ac06f59c902b05e089fd44d6c6f182153ef898afc7173d820c43e7898908d3843</originalsourceid><addsrcrecordid>eNqNkm9rFDEQxhdRbK39ChIQQZDT_N1NfFE41qqFK4KtfRuyyeyZcy85k92TfnvjXT1aX_lqhsnveZjMTFWdEvyWElW_Y1hQ9ag6JpzJmZQNeXwvP6qe5bzCmGKuyNPqiPG6plSR4ype-sGhudtCyoDOtxDGjGKPrjbTGPwPdINujLU-APIBfZ1y9uY9uorWmwFdgvMGtTGMRYXmwQy32e_U1zDAMpk12hbgA8AGLcCk4MPyefWkN0OG07t4Un37eH7dfp4tvny6aOeLmeWiGWfG4roXyipMOywAS9U7zl1t655ISgSDXippetuQhjlJseUMmlJSWDomOTupLva-LpqV3iS_NulWR-P1rhDTUps0ejuAttRJ0gihBOl5R5xx1hLKDHSUO1674nW299pM3RqcLb9NZnhg-vAl-O96Gbda1oxwiovB6zuDFH9OkEe99tnCMJgAccqaCsk4l5KJgr78B13FKZXRFqrGZW2CE1moV3vKpphzgv7QDMH6zzno3TkU7sX9zg_U3_0XQO6BX9DFPlsPwcIBwxg3jHKBRclw3frRjD6GNk5hLNI3_y9lvwEKBM46</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2604665418</pqid></control><display><type>article</type><title>Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central Open Access</source><source>Applied Social Sciences Index & Abstracts (ASSIA)</source><source>Web of Science - Science Citation Index Expanded - 2021<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" /></source><source>PubMed Central</source><source>Web of Science - Social Sciences Citation Index – 2021<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" /></source><creator>Jarynowski, Andrzej ; Semenov, Alexander ; Kaminski, Mikolaj ; Belik, Vitaly</creator><creatorcontrib>Jarynowski, Andrzej ; Semenov, Alexander ; Kaminski, Mikolaj ; Belik, Vitaly</creatorcontrib><description>Background: There is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs.
Objective: We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs.
Methods: We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multilabel classifications using the deep neural language model Bidirectional Encoder Representations from Transformers (BERT) "DeepPavlov," which was pretrained on a Russian language corpus and applied to the Telegram messages. The resulting area under the curve score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea.
Results: Telegram users complained mostly about pain (5461/11,515, 47.43%), fever (5363/11,515, 46.57%), fatigue (3862/11,515, 33.54%), and headache (2855/11,515, 24.79%). Women reported more AEs than men (1.2-fold, P<.001). In addition, there were more AEs from the first dose than from the second dose (1.1-fold, P<.001), and the number of AEs decreased with age (beta=.05 per year, P<.001). The results also showed that Sputnik V AEs were more similar to other vector vaccines (132 units) than with messenger RNA vaccines (241 units) according to the average Euclidean distance between the vectors of AE frequencies. Elderly Telegram users reported significantly more (5.6-fold on average) systemic AEs than their peers, according to the results of the phase 3 clinical trials published in The Lancet. However, the AEs reported in Telegram posts were consistent (Pearson correlation r=0.94, P=.02) with those reported in the Argentinian postmarketing AE registry.
Conclusions: After the Sputnik V vaccination, Russian Telegram users reported mostly pain, fever, and fatigue. The Sputnik V AE profile was comparable with other vector COVID-19 vaccines. Discussion on social media could provide meaningful information about the AE profile of novel vaccines.</description><identifier>ISSN: 1438-8871</identifier><identifier>ISSN: 1439-4456</identifier><identifier>EISSN: 1438-8871</identifier><identifier>DOI: 10.2196/30529</identifier><identifier>PMID: 34662291</identifier><language>eng</language><publisher>TORONTO: Jmir Publications, Inc</publisher><subject>Aged ; Bidirectionality ; Classification ; Clinical research ; Clinical trials ; Comorbidity ; Content analysis ; Coronaviruses ; COVID-19 ; COVID-19 Vaccines ; Critical incidents ; Datasets ; Deep Learning ; Diarrhea ; Dosage ; Fatigue ; Female ; Health Care Sciences & Services ; Humans ; Immunization ; Insomnia ; Labeling ; Learning ; Life Sciences & Biomedicine ; Male ; Medical Informatics ; Muscle pain ; Nausea ; Older people ; Original Paper ; Pain ; Russia ; Russian language ; SARS-CoV-2 ; Science & Technology ; Severe acute respiratory syndrome coronavirus 2 ; Social Media ; Social networks ; Vaccines ; Vaccines - adverse effects ; Vomiting</subject><ispartof>Journal of medical Internet research, 2021-11, Vol.23 (11), p.e30529-e30529, Article 30529</ispartof><rights>Andrzej Jarynowski, Alexander Semenov, Mikołaj Kamiński, Vitaly Belik. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 29.11.2021.</rights><rights>2021. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Andrzej Jarynowski, Alexander Semenov, Mikołaj Kamiński, Vitaly Belik. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 29.11.2021. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>20</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000732450500006</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c457t-ac06f59c902b05e089fd44d6c6f182153ef898afc7173d820c43e7898908d3843</citedby><cites>FETCH-LOGICAL-c457t-ac06f59c902b05e089fd44d6c6f182153ef898afc7173d820c43e7898908d3843</cites><orcidid>0000-0003-2691-4575 ; 0000-0003-3748-0071 ; 0000-0003-0949-6674 ; 0000-0002-4394-0460</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,315,728,781,785,865,886,2103,2115,12851,27929,27930,31004,39262,39263</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34662291$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Jarynowski, Andrzej</creatorcontrib><creatorcontrib>Semenov, Alexander</creatorcontrib><creatorcontrib>Kaminski, Mikolaj</creatorcontrib><creatorcontrib>Belik, Vitaly</creatorcontrib><title>Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning</title><title>Journal of medical Internet research</title><addtitle>J MED INTERNET RES</addtitle><addtitle>J Med Internet Res</addtitle><description>Background: There is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs.
Objective: We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs.
Methods: We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multilabel classifications using the deep neural language model Bidirectional Encoder Representations from Transformers (BERT) "DeepPavlov," which was pretrained on a Russian language corpus and applied to the Telegram messages. The resulting area under the curve score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea.
Results: Telegram users complained mostly about pain (5461/11,515, 47.43%), fever (5363/11,515, 46.57%), fatigue (3862/11,515, 33.54%), and headache (2855/11,515, 24.79%). Women reported more AEs than men (1.2-fold, P<.001). In addition, there were more AEs from the first dose than from the second dose (1.1-fold, P<.001), and the number of AEs decreased with age (beta=.05 per year, P<.001). The results also showed that Sputnik V AEs were more similar to other vector vaccines (132 units) than with messenger RNA vaccines (241 units) according to the average Euclidean distance between the vectors of AE frequencies. Elderly Telegram users reported significantly more (5.6-fold on average) systemic AEs than their peers, according to the results of the phase 3 clinical trials published in The Lancet. However, the AEs reported in Telegram posts were consistent (Pearson correlation r=0.94, P=.02) with those reported in the Argentinian postmarketing AE registry.
Conclusions: After the Sputnik V vaccination, Russian Telegram users reported mostly pain, fever, and fatigue. The Sputnik V AE profile was comparable with other vector COVID-19 vaccines. Discussion on social media could provide meaningful information about the AE profile of novel vaccines.</description><subject>Aged</subject><subject>Bidirectionality</subject><subject>Classification</subject><subject>Clinical research</subject><subject>Clinical trials</subject><subject>Comorbidity</subject><subject>Content analysis</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 Vaccines</subject><subject>Critical incidents</subject><subject>Datasets</subject><subject>Deep Learning</subject><subject>Diarrhea</subject><subject>Dosage</subject><subject>Fatigue</subject><subject>Female</subject><subject>Health Care Sciences & Services</subject><subject>Humans</subject><subject>Immunization</subject><subject>Insomnia</subject><subject>Labeling</subject><subject>Learning</subject><subject>Life Sciences & Biomedicine</subject><subject>Male</subject><subject>Medical Informatics</subject><subject>Muscle pain</subject><subject>Nausea</subject><subject>Older people</subject><subject>Original Paper</subject><subject>Pain</subject><subject>Russia</subject><subject>Russian language</subject><subject>SARS-CoV-2</subject><subject>Science & Technology</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Social Media</subject><subject>Social networks</subject><subject>Vaccines</subject><subject>Vaccines - adverse effects</subject><subject>Vomiting</subject><issn>1438-8871</issn><issn>1439-4456</issn><issn>1438-8871</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>GIZIO</sourceid><sourceid>HGBXW</sourceid><sourceid>EIF</sourceid><sourceid>7QJ</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>DOA</sourceid><recordid>eNqNkm9rFDEQxhdRbK39ChIQQZDT_N1NfFE41qqFK4KtfRuyyeyZcy85k92TfnvjXT1aX_lqhsnveZjMTFWdEvyWElW_Y1hQ9ag6JpzJmZQNeXwvP6qe5bzCmGKuyNPqiPG6plSR4ype-sGhudtCyoDOtxDGjGKPrjbTGPwPdINujLU-APIBfZ1y9uY9uorWmwFdgvMGtTGMRYXmwQy32e_U1zDAMpk12hbgA8AGLcCk4MPyefWkN0OG07t4Un37eH7dfp4tvny6aOeLmeWiGWfG4roXyipMOywAS9U7zl1t655ISgSDXippetuQhjlJseUMmlJSWDomOTupLva-LpqV3iS_NulWR-P1rhDTUps0ejuAttRJ0gihBOl5R5xx1hLKDHSUO1674nW299pM3RqcLb9NZnhg-vAl-O96Gbda1oxwiovB6zuDFH9OkEe99tnCMJgAccqaCsk4l5KJgr78B13FKZXRFqrGZW2CE1moV3vKpphzgv7QDMH6zzno3TkU7sX9zg_U3_0XQO6BX9DFPlsPwcIBwxg3jHKBRclw3frRjD6GNk5hLNI3_y9lvwEKBM46</recordid><startdate>20211129</startdate><enddate>20211129</enddate><creator>Jarynowski, Andrzej</creator><creator>Semenov, Alexander</creator><creator>Kaminski, Mikolaj</creator><creator>Belik, Vitaly</creator><general>Jmir Publications, Inc</general><general>Gunther Eysenbach MD MPH, Associate Professor</general><general>JMIR Publications</general><scope>17B</scope><scope>BLEPL</scope><scope>DTL</scope><scope>DVR</scope><scope>EGQ</scope><scope>GIZIO</scope><scope>HGBXW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QJ</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ALSLI</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>CNYFK</scope><scope>COVID</scope><scope>DWQXO</scope><scope>E3H</scope><scope>F2A</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M1O</scope><scope>NAPCQ</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-2691-4575</orcidid><orcidid>https://orcid.org/0000-0003-3748-0071</orcidid><orcidid>https://orcid.org/0000-0003-0949-6674</orcidid><orcidid>https://orcid.org/0000-0002-4394-0460</orcidid></search><sort><creationdate>20211129</creationdate><title>Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning</title><author>Jarynowski, Andrzej ; Semenov, Alexander ; Kaminski, Mikolaj ; Belik, Vitaly</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c457t-ac06f59c902b05e089fd44d6c6f182153ef898afc7173d820c43e7898908d3843</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Aged</topic><topic>Bidirectionality</topic><topic>Classification</topic><topic>Clinical research</topic><topic>Clinical trials</topic><topic>Comorbidity</topic><topic>Content analysis</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>COVID-19 Vaccines</topic><topic>Critical incidents</topic><topic>Datasets</topic><topic>Deep Learning</topic><topic>Diarrhea</topic><topic>Dosage</topic><topic>Fatigue</topic><topic>Female</topic><topic>Health Care Sciences & Services</topic><topic>Humans</topic><topic>Immunization</topic><topic>Insomnia</topic><topic>Labeling</topic><topic>Learning</topic><topic>Life Sciences & Biomedicine</topic><topic>Male</topic><topic>Medical Informatics</topic><topic>Muscle pain</topic><topic>Nausea</topic><topic>Older people</topic><topic>Original Paper</topic><topic>Pain</topic><topic>Russia</topic><topic>Russian language</topic><topic>SARS-CoV-2</topic><topic>Science & Technology</topic><topic>Severe acute respiratory syndrome coronavirus 2</topic><topic>Social Media</topic><topic>Social networks</topic><topic>Vaccines</topic><topic>Vaccines - adverse effects</topic><topic>Vomiting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jarynowski, Andrzej</creatorcontrib><creatorcontrib>Semenov, Alexander</creatorcontrib><creatorcontrib>Kaminski, Mikolaj</creatorcontrib><creatorcontrib>Belik, Vitaly</creatorcontrib><collection>Web of Knowledge</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Social Sciences Citation Index</collection><collection>Web of Science Primary (SCIE, SSCI & AHCI)</collection><collection>Web of Science - Social Sciences Citation Index – 2021</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Applied Social Sciences Index & Abstracts (ASSIA)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Social Science Premium Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Library & Information Science Collection</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>Library & Information Sciences Abstracts (LISA)</collection><collection>Library & Information Science Abstracts (LISA)</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Library Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Journal of medical Internet research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jarynowski, Andrzej</au><au>Semenov, Alexander</au><au>Kaminski, Mikolaj</au><au>Belik, Vitaly</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning</atitle><jtitle>Journal of medical Internet research</jtitle><stitle>J MED INTERNET RES</stitle><addtitle>J Med Internet Res</addtitle><date>2021-11-29</date><risdate>2021</risdate><volume>23</volume><issue>11</issue><spage>e30529</spage><epage>e30529</epage><pages>e30529-e30529</pages><artnum>30529</artnum><artnum>34662291</artnum><issn>1438-8871</issn><issn>1439-4456</issn><eissn>1438-8871</eissn><abstract>Background: There is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs.
Objective: We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs.
Methods: We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multilabel classifications using the deep neural language model Bidirectional Encoder Representations from Transformers (BERT) "DeepPavlov," which was pretrained on a Russian language corpus and applied to the Telegram messages. The resulting area under the curve score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea.
Results: Telegram users complained mostly about pain (5461/11,515, 47.43%), fever (5363/11,515, 46.57%), fatigue (3862/11,515, 33.54%), and headache (2855/11,515, 24.79%). Women reported more AEs than men (1.2-fold, P<.001). In addition, there were more AEs from the first dose than from the second dose (1.1-fold, P<.001), and the number of AEs decreased with age (beta=.05 per year, P<.001). The results also showed that Sputnik V AEs were more similar to other vector vaccines (132 units) than with messenger RNA vaccines (241 units) according to the average Euclidean distance between the vectors of AE frequencies. Elderly Telegram users reported significantly more (5.6-fold on average) systemic AEs than their peers, according to the results of the phase 3 clinical trials published in The Lancet. However, the AEs reported in Telegram posts were consistent (Pearson correlation r=0.94, P=.02) with those reported in the Argentinian postmarketing AE registry.
Conclusions: After the Sputnik V vaccination, Russian Telegram users reported mostly pain, fever, and fatigue. The Sputnik V AE profile was comparable with other vector COVID-19 vaccines. Discussion on social media could provide meaningful information about the AE profile of novel vaccines.</abstract><cop>TORONTO</cop><pub>Jmir Publications, Inc</pub><pmid>34662291</pmid><doi>10.2196/30529</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0003-2691-4575</orcidid><orcidid>https://orcid.org/0000-0003-3748-0071</orcidid><orcidid>https://orcid.org/0000-0003-0949-6674</orcidid><orcidid>https://orcid.org/0000-0002-4394-0460</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Aged Bidirectionality Classification Clinical research Clinical trials Comorbidity Content analysis Coronaviruses COVID-19 COVID-19 Vaccines Critical incidents Datasets Deep Learning Diarrhea Dosage Fatigue Female Health Care Sciences & Services Humans Immunization Insomnia Labeling Learning Life Sciences & Biomedicine Male Medical Informatics Muscle pain Nausea Older people Original Paper Pain Russia Russian language SARS-CoV-2 Science & Technology Severe acute respiratory syndrome coronavirus 2 Social Media Social networks Vaccines Vaccines - adverse effects Vomiting |
| title | Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-15T06%3A20%3A01IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Mild%20Adverse%20Events%20of%20Sputnik%20V%20Vaccine%20in%20Russia:%20Social%20Media%20Content%20Analysis%20of%20Telegram%20via%20Deep%20Learning&rft.jtitle=Journal%20of%20medical%20Internet%20research&rft.au=Jarynowski,%20Andrzej&rft.date=2021-11-29&rft.volume=23&rft.issue=11&rft.spage=e30529&rft.epage=e30529&rft.pages=e30529-e30529&rft.artnum=30529&rft.issn=1438-8871&rft.eissn=1438-8871&rft_id=info:doi/10.2196/30529&rft_dat=%3Cproquest_cross%3E2583448835%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2604665418&rft_id=info:pmid/34662291&rft_doaj_id=oai_doaj_org_article_c2d81755951f4b1dadcc123aeb24d46d&rfr_iscdi=true |